Effective training in sports activity, for example, needs a trainer who can correctly evaluate a body motion as a target. The number of such trainers is not enough than the sports population, and the evaluation depends on experiences or the like and is not necessarily constant. Meanwhile, attempts have been made for quantitative evaluation of a target motion from an engineering standpoint. Many of such attempts, however, are just superficial evaluation based on the performance of the overall motion or information from a sensor attached on a body surface.
Recently a system is described to estimate and visualize in real time the muscle action inside a human body during general motions (Non Patent Literature 1). This system includes a motion capture system detecting markers attached to a subject, a floor reaction force indicator including a plate-form pressure-sensing device to detect the motion of the subject and a computer that executes a given operation to the detected results. Such a system attracts much attention for the specific analysis ability of a body motion and its potential applicability to deep sense estimation. This system enables visualization of muscle tension during general motions mainly on the basis of kinesiological data.
Patent Literature 1 describes a technique of detecting a tension of each of a plurality of muscles based on myoelectric signals of the muscles measured using an electromyograph, and generating a machine learning model to estimate a time-series combination of a plurality of joint states on the basis of a history of a plurality of joint states decided from a tension of each muscle and tension of a plurality of muscles. According to this technique, the muscle motion is not limited, and a desired plurality of joint states and a time-series combination of these states can be precisely and quickly estimated on the basis of myoelectric signals of a plurality of muscles.
Non Patent Literature 2 describes the combination of muscle synergies for motion. More particularly, Non Patent Literature 2 discloses a method of converting time-series myoelectric signals each detected from thirteen muscles into a synthetic variable (score) represented by five types of main components by multivariate analysis. In this way, the number of types of variables in a control target model is reduced, whereby motion understanding and mechanical control for a model can be facilitated.